The
goal of this project is to machine-learn behavioral rules for
agent-based models, using data-mining and knowledge discovery on
massive databases of trajectory samples from diverse sources. These
data may come from location-aware hardware, such as Geographic
Positioning Systems, alternative positioning systems (Wi-Fi, cell-phone
triangulation), from geocoded trip diaries, or from observation. We are developing a scheme that can work with any of these data types, using only the simplest of geographical information: location in space and time.

This
will allow us to build agent-based models for situations in which no
theory exists, or to use machine-learning to better support
theory-driven models by allying them to the real-world behavioral
geography of actual people, in actual places, engaged in actual
activities.

The scheme works as a combination of
spatial database management, spatial data access, spatial analysis,
classification, clustering, and weighted training. Initially, we are
using data from a three-year observational study, for which we developed a customized space-time GIS observation and data-warehousing scheme.

Movie

Eye candy

The figure above illustrates sample trajectories from our three-year observational study, collected using a customized space-time GIS system that we developed to run on mobile hardware.

The
figure above illustrates the machine-learned agent-based model running
in real-time, constantly learning its path through space and time using
only a library of trajectory samples and our knowledge discovery model.